VLSI Design of a Large-Number Multiplier for Fully Homomorphic Encryption
By Wei Wang, Xinming Huang, Niall Emmart, C. Weems. 2014
68 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kim Laine
- Wei Dai
- M. Sadegh Riazi
- Blake Pelton
- Michael Naehrig
- Frederik Vercauteren
- Kimmo U. Järvinen
- Sujoy Sinha Roy
In Information Systems
- Michael Naehrig
- Frederik Vercauteren
- Kim Laine
- Wei Dai
- M. Sadegh Riazi
- Blake Pelton
- Mauro Conti
- Adrián Macías
Citations in the corpus
- HEAX: An Architecture for Computing on Encrypted Data (2019, methodology): “The security parameters for which we have instantiated HEAX are borrowed from the HE security standards [2] for 128-bit classical security.”
- A Survey on Homomorphic Encryption Schemes (2017, background): “Hence, it is suggested in some studies to use GPU order to improve the ef-ficiency of homomorphic evaluation [Dai et al. 2014; Wang et al. 2014; Wang et al. 2015; Dai et al. 2015; Lee et al. 2015].”
- HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, background): “A line of research focuses on designing very large integer multipliers – based on FPGAs or ASICs – that can be used to accelerate homomorphic operations [74, 75, 28, 29, 13].”
- Accelerating Homomorphic Evaluation on Reconfigurable Hardware (2015, background): “These may be reasons that previous work mainly focuses on using GPUs [19,40,41] and ASICs [21,44], and that FPGA implementations either work only with small parameters and on-chip memory [11] or explicitly do not take into account the complexity of transferring data between an FPGA and a host [9,35]”
- Modular Hardware Architecture for Somewhat Homomorphic Function Evaluation (2015, methodology): “Wang and Huang [39,40] later showed that this multiplication can be further accelerated by a factor of approximately two together with significant reductions in power consumption by using FPGA and ASIC.”
- HEPCloud: An FPGA-Based Multicore Processor for FV Somewhat Homomorphic Function Evaluation (2018, background): “Hence, it is not surprising that during the recent years several publications [8], [13], [14], [15], [26], [28], [31], [33], [40], [41] have reported re-sults on hardware-based acceleration of different FHE and SHE schemes or their central operations.”